code: def load_pandas_df(
      size="sample", local_cache_path=None, header=DEFAULT_HEADER
  ):
      """Loads the Criteo DAC dataset as `pandas.DataFrame`. This function download, untar, and load the dataset.
  
      The dataset consists of a portion of Criteo’s traffic over a period
      of 24 days. Each row corresponds to a display ad served by Criteo and the first
      column indicates whether this ad has been clicked or not.
  
      There are 13 features taking integer values (mostly count features) and 26
      categorical features. The values of the categorical features have been hashed
      onto 32 bits for anonymization purposes.
  
      The schema is:
  
      .. code-block:: python
  
          <label> <integer feature 1> ... <integer feature 13> <categorical feature 1> ... <categorical feature 26>
  
      More details (need to accept user terms to see the information):
      http://labs.criteo.com/2013/12/download-terabyte-click-logs/
  
      Args:
          size (str): Dataset size. It can be "sample" or "full".
          local_cache_path (str): Path where to cache the tar.gz file locally
          header (list): Dataset header names.
  
      Returns:
          pandas.DataFrame: Criteo DAC sample dataset.
      """
      with download_path(local_cache_path) as path:
          filepath = download_criteo(size, path)
          filepath = extract_criteo(size, filepath)
          df = pd.read_csv(
              filepath, sep="\t", header=None, names=header
          )
      return df
  
file_path: recommenders/recommenders/datasets/criteo.py
imports: import pandas
  import os
  import tarfile
  from recommenders.datasets.download_utils import maybe_download, download_path
  from recommenders.utils.notebook_utils import is_databricks
label_desc: Use image node_id 1 for calling a global function with example usage: load_pandas_df(size, local_cache_path, header) and returns: df